ClarusC64/clinical-recovery-policy-switching-v0.1
Clinical Recovery Policy Switching v0.1 This dataset tests whether a model can detect when the current clinical recovery policy is no longer the right frame. The task is not diagnosis. The task is not choosing the next local intervention. The task is deciding whether the whole recovery policy should continue, switch, stop, or escalate. Core idea A local intervention may still look plausible while the overall policy has failed. Example: iron_first may still be… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-recovery-policy-switching-v0.1.
Clinical Recovery Policy Switching v0.1
This dataset tests whether a model can detect when the current clinical recovery policy is no longer the right frame.
The task is not diagnosis.
The task is not choosing the next local intervention.
The task is deciding whether the whole recovery policy should continue, switch, stop, or escalate.
Core idea
A local intervention may still look plausible while the overall policy has failed.
Example:
iron_first may still be locally reasonable,
but if the system has crossed into bandwidth collapse,
the correct policy is stabilization or acute escalation.
Prediction target
Predict:
policy_decision
Prediction files should contain:
scenario_id,prediction
TE001,continue_recovery_policy
Allowed labels
continue_recovery_policy
switch_to_stabilization_policy
switch_to_resource_rebuild_policy
switch_to_sleep_repair_policy
switch_to_load_shedding_policy
escalate_to_acute_policy
stop_intervention_policy
Row structure
Each row contains:
current policy
policy fit score
regime transition score
policy failure signal
rescue window remaining
adaptive bandwidth
constraint volatility
recent feedback quality
local action availability
gold policy decision
label outcome
Why this is difficult
Most models optimize inside the current frame.
This benchmark asks whether the model can detect frame failure.
The correct answer may be:
stop optimizing the old policy
switch policy
escalate
or stop intervening
Evaluation
Run:
python scorer.py predictions.csv data/test.csv
The scorer reports:
decision accuracy
switch accuracy
continue accuracy
acute escalation accuracy
stop policy accuracy
regime transition safety score
rescue window policy score
over-switch resistance
macro precision
macro recall
macro F1
structural score
The main metric is:
structural_score
Structural Note
This dataset is synthetic.
It is designed to test recovery policy switching under regime transition and frame failure.
It is not medical advice and should not be used for clinical decision-making.
License
MIT